PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks

Zhao, Leyang; Yang, Yanguang; Ma, Ding; Lin, Xing; Wang, Wang

doi:https://doi.org/10.5194/isprs-annals-X-G-2025-1045-2025

Articles | Volume X-G-2025

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Articles | Volume X-G-2025

https://doi.org/10.5194/isprs-annals-X-G-2025-1045-2025

Articles | Volume X-G-2025

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14 Jul 2025

| 14 Jul 2025

PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks

Leyang Zhao, Yanguang Yang, Ding Ma, Xing Lin, and Wang Wang

Leyang Zhao

School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China

Yanguang Yang

School of Geomatics and Geographical Sciences, Liaoning Technical University, Fuxin 123000, China

Ding Ma

School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China

Xing Lin

School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China

Wang Wang

School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China

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PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks

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